Detecting memory management anti-patterns

Abstract

The present invention provides a method, system, and computer program product for detecting memory management anti-patterns. The method comprises: periodically generating census data for at least one object type in a heap; collecting the periodically generated census data for each object type into a time series; analyzing each time series; and classifying each time series into one of a plurality of different categories based on the analysis.

Description

BACKGROUND OF THE INVENTION[0001]1. Field of the Invention[0002]The present invention generally relates to memory management. More particularly, the present invention provides a method, system, and computer program product for detecting performance-degrading memory management anti-patterns, particularly in systems with automatic storage reclamation (“garbage collection”).[0003]2. Related Art[0004]A pattern is a common solution to a problem that occurs in many different contexts. Patterns capture expert knowledge about “best practices” in software design in a form that allows that knowledge to be reused and applied in the design of many different types of software. Anti-patterns are conceptually similar to patterns in that they document recurring solutions to common design problems. They are known as anti-patterns because their use (or misuse) produces negative consequences. Anti-patterns document common mistakes made during software development as well as their solutions. Anti-patte...

AI Technical Summary

Problems solved by technology

They are known as anti-patterns because their use (or misuse) produces negative consequences.

When the object is no longer needed, necessary clean-up must be performed and the reclaimed memory must be returned to the heap to avoid “memory leaks.” While the overhead for creating and destroying a single object may be small, when a large number of short-lived objects are frequently created and then destroyed (also referred to as “object churn”) the performance impact may be significant, and memory leaks resulting in out-of-control memory growth may occur.

Procedures based on this strategy can only detect long-lived unnecessary object retention.

Another class of memory management problems (uncontrolled heap growth) arises from short-lived object retention due, for example, to object churn and other transient memory requirements that increase process size (footprint) and related processing costs.

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